{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T14:31:39.791506Z",
     "start_time": "2020-04-26T14:31:38.127985Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tqdm import tqdm_notebook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Hyperparameters\n",
    "# batch_size = 32\n",
    "epochs = 5000\n",
    "lr = 0.01\n",
    "\n",
    "# Using GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:22:54.225199Z",
     "start_time": "2020-04-26T15:22:54.212231Z"
    }
   },
   "outputs": [],
   "source": [
    "def multi_train(model_list, epochs, lr_list, X_train, y_train, X_test, y_test):\n",
    "    def model_train(model, criterion, optimizer, epochs, X_train, y_train, X_test, y_test):\n",
    "        def model_eval(model, X_test, y_test):\n",
    "            model.eval()\n",
    "\n",
    "            output = model(X_test.to(device))\n",
    "            loss = criterion(output, y_test.to(device))\n",
    "            val_loss = loss.item()\n",
    "            predicted = output.data\n",
    "            val_correct = (torch.argmax(predicted.cpu(), axis=1) == y_test.cpu()).sum().item()\n",
    "            val_total = y_test.size(0)\n",
    "\n",
    "            print('val_loss: %.03f | val_acc: %.3f'\n",
    "                  % (val_loss, val_correct / val_total))\n",
    "        \n",
    "        train_loss = []\n",
    "        train_acc = []\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "            model.train() \n",
    "            optimizer.zero_grad()\n",
    "            output = model(X_train.to(device))\n",
    "            loss = criterion(output, y_train.to(device))\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss.append(loss.item())\n",
    "            predicted = output.data\n",
    "            train_correct = (torch.argmax(predicted.cpu(), axis=1) == y_train.cpu()).sum().item()\n",
    "            train_total = y_train.size(0)\n",
    "            train_acc.append(train_correct / train_total)\n",
    "        \n",
    "        if X_test != None and y_test != None:\n",
    "            model_eval(model, X_test, y_test)\n",
    "            \n",
    "        return train_loss, train_acc\n",
    "\n",
    "    loss_records = []\n",
    "    acc_records = []\n",
    "    \n",
    "    for i in tqdm_notebook(range(len(model_list))):\n",
    "        model = model_list[i]\n",
    "        model = model.to(device)\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        optimizer = optim.Adam(model.parameters(), lr=lr_list[i])\n",
    "        train_loss, train_acc = model_train(model, criterion, optimizer, epochs, X_train, y_train, X_test, y_test)\n",
    "        loss_records.append(train_loss)\n",
    "        acc_records.append(train_acc)\n",
    "    \n",
    "    return loss_records, acc_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:01:17.144385Z",
     "start_time": "2020-04-26T15:01:17.118423Z"
    }
   },
   "outputs": [],
   "source": [
    "def exp_net_depth(num_input, num_output, X_train, y_train, X_test=None, y_test=None):\n",
    "    model1_16 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output)\n",
    "    )\n",
    "\n",
    "    model2_16 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output)\n",
    "    )\n",
    "\n",
    "    model4_16 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output)\n",
    "    )\n",
    "\n",
    "    model8_16 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output)\n",
    "    )\n",
    "\n",
    "    model16_16 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output)\n",
    "    )\n",
    "\n",
    "    model32_16 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output)\n",
    "    )\n",
    "\n",
    "    model_list = [model1_16, model2_16, model4_16, model8_16, model16_16, model32_16]\n",
    "\n",
    "    loss_records, acc_records = multi_train(model_list, epochs, [0.001]*6, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    return loss_records, acc_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:01:18.038102Z",
     "start_time": "2020-04-26T15:01:18.030125Z"
    }
   },
   "outputs": [],
   "source": [
    "def exp_hidden_neuron(num_input, num_output, X_train, y_train, X_test=None, y_test=None):\n",
    "    model1_2 = nn.Sequential(\n",
    "        nn.Linear(num_input, 2),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(2, num_output),\n",
    "    )\n",
    "\n",
    "    model1_4 = nn.Sequential(\n",
    "        nn.Linear(num_input, 4),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(4, num_output),\n",
    "    )\n",
    "\n",
    "    model1_8 = nn.Sequential(\n",
    "        nn.Linear(num_input, 8),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(8, num_output),\n",
    "    )\n",
    "\n",
    "    model1_16 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output),\n",
    "    )\n",
    "\n",
    "    model1_32 = nn.Sequential(\n",
    "        nn.Linear(num_input, 32),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(32, num_output),\n",
    "    )\n",
    "\n",
    "    model_list = [model1_2, model1_4, model1_8, model1_16, model1_32]\n",
    "    loss_records, acc_records = multi_train(model_list, epochs, [0.001]*5, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    return loss_records, acc_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:01:18.799293Z",
     "start_time": "2020-04-26T15:01:18.782340Z"
    }
   },
   "outputs": [],
   "source": [
    "def exp_lr(num_input, num_output, X_train, y_train, X_test=None, y_test=None):\n",
    "    model1 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output),\n",
    "    )\n",
    "\n",
    "    model2 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output),\n",
    "    )\n",
    "\n",
    "    model3 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output),\n",
    "    )\n",
    "\n",
    "    model4 = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output),\n",
    "    )\n",
    "\n",
    "    model_list = [model1, model2, model3, model4]\n",
    "\n",
    "    loss_records, acc_records = multi_train(model_list, epochs, [0.0001, 0.001, 0.01, 0.1], X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    return loss_records, acc_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:01:19.499109Z",
     "start_time": "2020-04-26T15:01:19.486147Z"
    }
   },
   "outputs": [],
   "source": [
    "def exp_activation(num_input, num_output, X_train, y_train, X_test=None, y_test=None):\n",
    "    model_relu = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(16, num_output),\n",
    "    )\n",
    "\n",
    "    model_tanh = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Tanh(),\n",
    "        nn.Linear(16, num_output),\n",
    "    )\n",
    "\n",
    "    model_sigmoid = nn.Sequential(\n",
    "        nn.Linear(num_input, 16),\n",
    "        nn.Sigmoid(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Sigmoid(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Sigmoid(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Sigmoid(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Sigmoid(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Sigmoid(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Sigmoid(),\n",
    "        nn.Linear(16, 16),\n",
    "        nn.Sigmoid(),\n",
    "        nn.Linear(16, num_output),\n",
    "    )\n",
    "\n",
    "    model_list = [model_relu, model_tanh, model_sigmoid]\n",
    "\n",
    "    loss_records, acc_records = multi_train(model_list, epochs, [0.001]*3, X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    return loss_records, acc_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T14:31:43.754382Z",
     "start_time": "2020-04-26T14:31:43.742380Z"
    }
   },
   "outputs": [],
   "source": [
    "def iris_data_generator(normalize=True):\n",
    "    def data_normalize(X_train, X_test):\n",
    "        sc = StandardScaler()\n",
    "        sc.fit(X_train)\n",
    "        _X_train = sc.transform(X_train)\n",
    "        _X_test = sc.transform(X_test)\n",
    "\n",
    "        return _X_train, _X_test\n",
    "\n",
    "    iris = pd.read_csv('iris.csv', usecols=[1, 2, 3, 4, 5])\n",
    "    X = np.array(\n",
    "        iris[['Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width']], dtype=np.float32)\n",
    "    y = iris[['Species']].copy()\n",
    "    y[y['Species'] == 'setosa'] = 0\n",
    "    y[y['Species'] == 'versicolor'] = 1\n",
    "    y[y['Species'] == 'virginica'] = 2\n",
    "    y = np.array(y).reshape(-1)\n",
    "\n",
    "    X0_train, X0_test, y0_train, y0_test = train_test_split(X[np.where(y == 0)],\n",
    "                                                            y[np.where(\n",
    "                                                                y == 0)],\n",
    "                                                            test_size=20, random_state=1)\n",
    "    X1_train, X1_test, y1_train, y1_test = train_test_split(X[np.where(y == 1)],\n",
    "                                                            y[np.where(\n",
    "                                                                y == 1)],\n",
    "                                                            test_size=20, random_state=2)\n",
    "    X2_train, X2_test, y2_train, y2_test = train_test_split(X[np.where(y == 2)],\n",
    "                                                            y[np.where(\n",
    "                                                                y == 2)],\n",
    "                                                            test_size=20, random_state=3)\n",
    "    X_train = np.concatenate((X0_train, X1_train, X2_train), axis=0)\n",
    "    y_train = np.concatenate((y0_train, y1_train, y2_train))\n",
    "    X_test = np.concatenate((X0_test, X1_test, X2_test), axis=0)\n",
    "    y_test = np.concatenate((y0_test, y1_test, y2_test))\n",
    "    X_train, y_train = shuffle(X_train, y_train)\n",
    "    X_test, y_test = shuffle(X_test, y_test)\n",
    "\n",
    "    if normalize == True:\n",
    "        X_train, X_test = data_normalize(X_train, X_test)\n",
    "\n",
    "    return torch.from_numpy(X_train), torch.from_numpy(y_train), torch.from_numpy(X_test), torch.from_numpy(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第一题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T14:31:45.172962Z",
     "start_time": "2020-04-26T14:31:45.166980Z"
    }
   },
   "outputs": [],
   "source": [
    "X1 = torch.tensor([[3, 0.4],\n",
    "                   [1, 1],\n",
    "                   [3, 3],\n",
    "                   [2, 0.5],\n",
    "                   [3, 1],\n",
    "                   [1, 3],\n",
    "                   [1, 2],\n",
    "                   [2, 2],\n",
    "                   [3, 2]])\n",
    "y1 = torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2], dtype=torch.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-18T14:48:43.996960Z",
     "start_time": "2020-04-18T14:48:43.993027Z"
    }
   },
   "source": [
    "## 讨论隐藏层数对网络性能的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T14:40:32.554037Z",
     "start_time": "2020-04-26T14:33:13.152560Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "431658ea36584593b0014d3d327db9f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_net_depth(2, 3, X1, y1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].plot(loss_records[4])\n",
    "ax[0].plot(loss_records[5])\n",
    "ax[0].legend(('hidden_depth=1', 'hidden_depth=2', 'hidden_depth=4', 'hidden_depth=8', 'hidden_depth=16', 'hidden_depth=32'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].plot(acc_records[4])\n",
    "ax[1].plot(acc_records[5])\n",
    "ax[1].legend(('hidden_depth=1', 'hidden_depth=2', 'hidden_depth=4', 'hidden_depth=8', 'hidden_depth=16', 'hidden_depth=32'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讨论隐藏层神经元个数对网络性能的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T14:41:50.342640Z",
     "start_time": "2020-04-26T14:40:33.045723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2154b4335df9418a88ac61a4373893b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_hidden_neuron(2, 3, X1, y1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].plot(loss_records[4])\n",
    "ax[0].legend(('neuron=1', 'neuron=2', 'neuron=4', 'neuron=8', 'neuron=16'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].plot(acc_records[4])\n",
    "ax[1].legend(('neuron=1', 'neuron=2', 'neuron=4', 'neuron=8', 'neuron=16'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 学习率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T14:45:46.042127Z",
     "start_time": "2020-04-26T14:41:50.771494Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a86163f9193c4205aab8327eccdeb96f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_lr(2, 3, X1, y1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].legend(('lr=0.0001', 'lr=0.001', 'lr=0.01', 'lr=0.1'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].legend(('lr=0.0001', 'lr=0.001', 'lr=0.01', 'lr=0.1'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 激活函数的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T14:48:41.156041Z",
     "start_time": "2020-04-26T14:45:46.392214Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "642c18521e5a48d09aeec564c0e2736b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_activation(2, 3, X1, y1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].legend(('ReLU', 'Tanh', 'Sigmoid'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].legend(('ReLU', 'Tanh', 'Sigmoid'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T14:48:41.449265Z",
     "start_time": "2020-04-26T14:48:41.442288Z"
    }
   },
   "outputs": [],
   "source": [
    "X2 = torch.tensor([[0, 1, 0, 0, 1, 0, 0, 1, 0],\n",
    "                   [1, 1, 1, 0, 1, 0, 0, 1, 0],\n",
    "                   [1, 0, 1, 1, 0, 1, 1, 1, 1]], dtype=torch.float)\n",
    "y2 = torch.tensor([0, 1, 2], dtype=torch.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讨论隐藏层数对网络性能的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:09:07.362240Z",
     "start_time": "2020-04-26T15:01:30.654955Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7500bb55d87840169ebb639e9ab0f4ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_net_depth(9, 3, X2, y2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].plot(loss_records[4])\n",
    "ax[0].plot(loss_records[5])\n",
    "ax[0].legend(('hidden_depth=1', 'hidden_depth=2', 'hidden_depth=4', 'hidden_depth=8', 'hidden_depth=16', 'hidden_depth=32'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].plot(acc_records[4])\n",
    "ax[1].plot(acc_records[5])\n",
    "ax[1].legend(('hidden_depth=1', 'hidden_depth=2', 'hidden_depth=4', 'hidden_depth=8', 'hidden_depth=16', 'hidden_depth=32'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讨论隐藏层神经元个数对网络性能的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:10:30.074591Z",
     "start_time": "2020-04-26T15:09:07.861174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fe4da23a25f44c4aefe54ce7d24b0ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_hidden_neuron(9, 3, X2, y2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].plot(loss_records[4])\n",
    "ax[0].legend(('neuron=1', 'neuron=2', 'neuron=4', 'neuron=8', 'neuron=16'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].plot(acc_records[4])\n",
    "ax[1].legend(('neuron=1', 'neuron=2', 'neuron=4', 'neuron=8', 'neuron=16'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 学习率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:14:31.627941Z",
     "start_time": "2020-04-26T15:10:30.425662Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f56f333a0ef4711ba5db758add73bac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_lr(9, 3, X2, y2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].legend(('lr=0.0001', 'lr=0.001', 'lr=0.01', 'lr=0.1'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].legend(('lr=0.0001', 'lr=0.001', 'lr=0.01', 'lr=0.1'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 激活函数的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:17:45.841688Z",
     "start_time": "2020-04-26T15:14:31.971011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc9e180c929c4eee9321e8baa35973eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_activation(9, 3, X2, y2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].legend(('ReLU', 'Tanh', 'Sigmoid'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].legend(('ReLU', 'Tanh', 'Sigmoid'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:17:46.588179Z",
     "start_time": "2020-04-26T15:17:46.111966Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = iris_data_generator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讨论隐藏层数对网络性能的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:30:37.343812Z",
     "start_time": "2020-04-26T15:23:01.084747Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "babde79995024e149f95318c795190b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.186 | val_acc: 0.967\n",
      "val_loss: 0.244 | val_acc: 0.983\n",
      "val_loss: 0.419 | val_acc: 0.983\n",
      "val_loss: 0.592 | val_acc: 0.967\n",
      "val_loss: 0.802 | val_acc: 0.967\n",
      "val_loss: 1.099 | val_acc: 0.333\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_net_depth(4, 3, X_train, y_train, X_test, y_test)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].plot(loss_records[4])\n",
    "ax[0].plot(loss_records[5])\n",
    "ax[0].legend(('hidden_depth=1', 'hidden_depth=2', 'hidden_depth=4', 'hidden_depth=8', 'hidden_depth=16', 'hidden_depth=32'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].plot(acc_records[4])\n",
    "ax[1].plot(acc_records[5])\n",
    "ax[1].legend(('hidden_depth=1', 'hidden_depth=2', 'hidden_depth=4', 'hidden_depth=8', 'hidden_depth=16', 'hidden_depth=32'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讨论隐藏层神经元个数对网络性能的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:31:58.573695Z",
     "start_time": "2020-04-26T15:30:37.884428Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23cec242bdd044aa82b5d58d72d0c507",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.077 | val_acc: 0.967\n",
      "val_loss: 0.088 | val_acc: 0.967\n",
      "val_loss: 0.235 | val_acc: 0.950\n",
      "val_loss: 0.254 | val_acc: 0.950\n",
      "val_loss: 0.222 | val_acc: 0.967\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_hidden_neuron(4, 3, X_train, y_train, X_test, y_test)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].plot(loss_records[4])\n",
    "ax[0].legend(('neuron=1', 'neuron=2', 'neuron=4', 'neuron=8', 'neuron=16'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].plot(acc_records[4])\n",
    "ax[1].legend(('neuron=1', 'neuron=2', 'neuron=4', 'neuron=8', 'neuron=16'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 学习率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:35:56.723928Z",
     "start_time": "2020-04-26T15:31:59.051392Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67dc09a73389416a80159da0bd6d8593",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.220 | val_acc: 0.967\n",
      "val_loss: 0.593 | val_acc: 0.967\n",
      "val_loss: 2.194 | val_acc: 0.950\n",
      "val_loss: 2.383 | val_acc: 0.967\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_lr(4, 3, X_train, y_train, X_test, y_test)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].plot(loss_records[3])\n",
    "ax[0].legend(('lr=0.0001', 'lr=0.001', 'lr=0.01', 'lr=0.1'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].plot(acc_records[3])\n",
    "ax[1].legend(('lr=0.0001', 'lr=0.001', 'lr=0.01', 'lr=0.1'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 激活函数的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T15:38:49.837022Z",
     "start_time": "2020-04-26T15:35:57.148776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6447c950350a4e869de443cd1647e2f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.835 | val_acc: 0.967\n",
      "val_loss: 0.303 | val_acc: 0.967\n",
      "val_loss: 0.241 | val_acc: 0.967\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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gBB2KKuhJ2htjbK6ggq4WdxERkcljIJUBoCHq7fhiJqk16DKq/Br0aESNqCJSffrNAsMq6C0NUbYPpsc+vgS1uIuIiEweffkEPVYiQe/frBZ32YFzjk0Dm9jUvwlQi7uI1IYSdBg2JK45EaUvmSGbdUQio+yNWoJa3EVERCaPS3/6OAAtDSO+Cr10PwxsAZv4dwCZHq75yzV867FvFR43RZsCjEZEpqqaJehmdj3wTmCdc27/Eq8b8HXgBKAPONc590it4hlT0TZrLQn/Svr2ZJrWholvnxGLxIhH4mxPK0EXEREJu60DfrfcMYvmDH9hy8v+7dIz6hyRhF33tm7aE+187KCPMatxFnOa54z/JhGRMtWygn4D8E3ge6O8fjywT+7nMODbudv6G1FBB9g+mCkrQQe/zb0v1Vft6ERERKTKBlMZ3rpvF43xES3uuS20mLGg7jFJuA2kB5iRmMGZ+50ZdCgiMoXVbEicc+5eYNMYh5wCfM/57gc6zGzXWsUzpvyk1vQgLfkEPVn+OvSmWJNa3EVEJoEvfelLLFmyhAMOOICDDjqIBx54gPPPP58nn3yypn/uCSecMGyrl7zLLruMK6+8sqZ/tgw3kMrSUGoP9Mygf6s16DLCYGaQhnxRR0QCN1XP5UGuQZ8HvFz0uDv33GsjDzSzC4ALAHbffffqR1JUQW+K5yvolQ2KUwVdRCTc/vSnP/Hzn/+cRx55hEQiwYYNG0gmk1x33XU1/7PvuOOOmv8ZMr5M1vHM69t4wy4ltljbsNq/jSpBlyFbk1u5p/seDuw6MOhQRISpfS4Pcpu1UtNXXKkDnXPXOueWO+eWd3V1VT8Sb2gNenNuDXpvBQl6g9fAYP7Ku4iIhNJrr71GZ2cniYT/u7+zs5O5c+eyYsUKVq1aBcB3vvMd9t13X1asWMGHP/xhLrzwQgDOPfdcPvrRj3LUUUex5557cs8993DeeeexaNEizj333MKfcdNNN7F06VL2339/PvWpTxWeX7BgARs2bAD8K/9veMMbOProo3nmmWfq9LcXgCde7QFGmQN3/9X+rSZ0S5FfvfgrALoaa/A9VETKNpXP5UFW0LuB3YoezwdeDSSSSMQ/EacHhlrcBzNlf0wimmAgM1Dt6EREpqyvPvhVnt70dFU/c7+Z+/GpQz816uvHHnssl19+Ofvuuy9HH300Z555JkceeWTh9VdffZUvfOELPPLII7S2tvK2t72NAw8cqppt3ryZ3/3ud9x+++2cdNJJ3HfffVx33XUccsghPPbYY8yePZtPfepTPPzww8yYMYNjjz2Wn/zkJ5x66qmFz3j44Ye5+eabefTRR0mn07zxjW9k2bJlVf13kNH15gbErTxkjK48JehSpDfVC8AX3/zFgCMRCZcgzuMwtc/lQVbQbwc+YL6/A3qcczu0t9dNJAbZdGFIXF8Fa9AbvAYG0krQRUTCrKWlhYcffphrr72Wrq4uzjzzTG644YbC6w8++CBHHnkkM2fOJBaL8e53v3vY+0866STMjKVLlzJnzhyWLl1KJBJhyZIlrFmzhoceeogVK1bQ1dVFNBrlve99L/fee++wz/jDH/7AaaedRlNTE21tbZx88sn1+KtLzkA6vwf6GF+DvPIGxcrUNpj2OyQbPK1BFwmDqXwur+U2azcBK4BOM+sGLgViAM65a4A78LdYW42/zdoHaxXLhHhRyKRozq1Br6jFPaoWdxGRcox3hbxWPM9jxYoVrFixgqVLl3LjjTcWXnOu5Gqrgnw7XSQSKdzPP06n00SjEzu1mvbZDsxAKgtAQ8wb/SD995EiA5kBopEoXmSM/2dEpqGgzuMwdc/ltZzifpZzblfnXMw5N9859x3n3DW55Jzc9PZ/cM7t5Zxb6pxbVatYJiQSg2yqsAa9kiFxCS+hCrqISMg988wzPPfcc4XHjz32GHvssUfh8aGHHso999zD5s2bSafT3HrrrWV9/mGHHcY999zDhg0byGQy3HTTTcPa7gDe+ta3ctttt9Hf38+2bdv42c9+tnN/KSnL8+v8duUxE3SZ9q548AqW3riUa/9yLdc/fj3pbPnfDUWkNqbyuTzINejh4sWGVdArWYPeEG3QGnQRkZDr7e3lYx/7GFu2bCEajbL33ntz7bXXcsYZZwAwb948PvOZz3DYYYcxd+5cFi9eTHt7+4Q/f9ddd+UrX/kKRx11FM45TjjhBE455ZRhx7zxjW/kzDPP5KCDDmKPPfbgLW95S1X/jjK2fMFjTpsmtcvofvDUDwD4z0f/M+BIRGSkqXwut/HK/2GzfPlyl5/MV1VXLYUFR8Bp17Dvv/ySDx6xgE8fv6isj/jqg1/lp6t/yh/P/mP14xMRmSKeeuopFi0q7/drvfX29tLS0kI6nea0007jvPPO47TTTgskllL/Xmb2sHNueSABVUHNzuUTdOWdz/Ct36/m+S+fsGN74ld2h4POhuOvCCY4CY2lNy7d4bm/nvPXACIRCZfJcB6HyXsuD3JIXLjk1qADNMY8BpIVTHH3EvRn+qsdmYiI1Nlll13GQQcdxP7778/ChQuHTW2VyW8glaEh5pVeO5hJakCciMgUMFnP5Wpxz8utQQc/Qe9PVdbins6myWQzGiIiIjKJXXnllUGHIDW0PZkhHi1Ro8hmId2vLdZERKaAyXouVwU9z4tBxh/+0Rj36M9NeC1HfusNTXIXERnbZFteFRT9O9XG46/0UHLu7g0n+rdK0KWE/WbuF3QIIqGh89PElftvpQQ9LxItVNAbYh79lbS4R/1hM/1ptbmLiIymoaGBjRs36uQ+DuccGzdupKFB+y5XW0dTjMZSE9xfys2QUYu7lHDTiTcFHYJIKOg8PnGVnMvV4p6Xm+IO0BiLMJiuoMVdFXQRkXHNnz+f7u5u1q9fH3QoodfQ0MD8+fMD+/PN7Djg64AHXOecu2LE63sA1wNdwCbgfc657roHWqZkOstuM5tGP0AVdBnhTXPfRDSir80ioPN4uco9l+s3TV4kBrn9LSutoDdE/QRdW62JiIwuFouxcOHCoMOQcZiZB1wNHAN0Aw+Z2e3OuSeLDrsS+J5z7kYzexvwFeD99Y+2PKlMlubEGF+BlKDLCLGIuipE8nQery21uOeNmOJeyZC4hOe3uA+klaCLiMikdyiw2jn3gnMuCdwMnDLimMXAb3P37y7xeiilMo6Y538F+uMrf+T5Lc9DcaumWtxlhLgu2ohInShBzytegx6vfIo7qMVdRESmhHnAy0WPu3PPFfszcHru/mlAq5nNKvVhZnaBma0ys1VBt0WmMllinj8m7iO/+QgX3HUBPH7r0AGu/EGxMrWpgi4i9aIEPS8Sq8o+6KAEXUREpoRSg85HTgT6JHCkmT0KHAm8AqRLfZhz7lrn3HLn3PKurq7qRlqmZCZbqKADrOtfBz1FS+ez5X8HkKll5PArVdBFpF60Bj3PixVOyI0xj4F0+VfP4xH/l3cyk6xqaCIiIgHoBnYrejwfeLX4AOfcq8C7AMysBTjdOddTtwgrlMpkiXsjahTJ3qH7Tgn6dOdGXItSBV1E6kUV9LyiFvfGeGVD4vJXV5Wgi4jIFPAQsI+ZLTSzOLASuL34ADPrNLP8d4lP4090D71U2g2roAMwWJSgq4I+7WVHLHNQBV1E6kUJel7RNmuJaISBdKbsvf2UoIuIyFThnEsDFwJ3Ak8BP3LOPWFml5vZybnDVgDPmNmzwBzgS4EEW6ZUJkssOqKDP7V96L6GxE17Iyvo+S5JEZFaU4t7XtE2a3EvgnOQzrrCEJmJKCToWSXoIiIy+Tnn7gDuGPHc54ru/xj4cb3j2lkj16ADMPeN8Mj3/PtL313/oCRURhZpEtFEQJGIyHSjCnpe0TZrsaj/z5LKlLcOXWvQRUREwq/kGvRiTTPrF4yE0sgK+n4z9gsoEhGZbpSg50VihTXo+ZN2ssxBcWpxFxERCb/ifdALNBhOioxcg15yTwMRkRpQgp7nxSCTa3GP7mSCrhZ3ERGRUMpkHZlsiQQ9q73PZUi5c4hERKpFCXpeJDpsDTr4a9TKoRZ3ERGRcPuvP7wAQHPCG/6CKuhSJN/i3hpvBWBmg5Y9iEh9aEhcXtE2a5VW0L2Ih2eeEnQREZGQ2tg7CMDKQ3cf/kJ+a7UP313niCSM8hX0c5ecy9LOpRzUdVDAEYnIdKEEPc8rmuIerayCDn6buxJ0ERGRcEplHK0NUVoSI74C5SvonfvWPygJnSz+d8AGr4HD5x4ecDQiMp2oxT0vEgWXhezQ1iupdPnrj+JeXGvQRUREQmrUCe75CnrE2/E1mXbyFfSI6auyiNSXfuvkRXJX0rPpogp6+evR4hFV0EVERILw55e38PTarWMek844ol6Jkdz5CropQZehBN1M49tFpL7U4p5XSNBThSvrg2WuQQe/gp7KrWUXERGR+jnl6vsAWHPFiaMekyrqlBs2qTs/xV0VdGFoSJxpfzURqTNV0POGVdD9X8apTGUt7oOZwWpGJiIiIlVSvAd6PgnzH+Qr6PpqJEP7oKuCLiL1prNQnhfzb7MZ4p5/9bzcKe6gFncREZEwS2eyRCN+0jW8gp7xk3MlZMLQxZuIviqLSJ3pt05evqUtk6p4mzXQkDgREZEwS2WGWtzzk7oBv4Ku9eeSozXoIhIUJeh5kXwFPU3My7e4l5+gxyIxVdBFRERCym9xzyVdxSvZXn/S381FBOhL9wUdgohMU0rQ84qHxO1EBT3hJZSgi4iIhFQ6O0oFvbFjaB26THu9qd6gQxCRaUoJel4hQc8UEvTBCirocU9r0EVERMIqlR7aZm3YGvRMCmbtHVBUEjapjL8jz7yWeQFHIiLTjRL0PG9oinsiNyQupW3WREREppTibdayxS3t2dTQcjeZ9vLf5WL6f0JE6kwJel6+gp5JEctts5ascA26tlkTERGps+R2PMZvUS8eEjdMJj10sV6mvUKC7ilBF5H6UoKeVzQkLu5pDbqIiMik8uW5XBv7j3EPS2dcYZu1YRX0TBK8eK2ik0kmnU0DqqCLSP0pQc+LDLW4R70IEat8mzW1uIuIiNTf271Hxz0mlckSy82accVj3NXiLkXya9CVoItIvSlBzytagw4Q8yLaZk1ERGSKSWUcsZIV9DSonVlytAZdRIKiBD2vaA06QNyLVLQGPeEltAZdREQkpNKjrUHPpoa+C8i0pwRdRIKiBD0vMryCHvWMdMaN8YbS8i3uw7ZuERERkVBIZhzRUlPcMylV0KVAQ+JEJChK0PMKQ+L8CbBRL0I6W9kadEDr0EVEREIonc0S80q0uGfTWoMuBfk16FF1VYhInSlBz4v4e59TaGkyUhVU0POtUFqHLiIiEj6p9FCLe8YVbcuWSaqCLgVqcReRoChBz/OGtlkD8Dwjky0/QU94CQCtQxcREQmhVNYRzVXQ81tpAWpxl2GUoItIUJSg540YEheLVDbFXS3uIiIi4ZXOZInnKujDEnS1uEsRrUEXkaDUNEE3s+PM7BkzW21ml5R4fXczu9vMHjWzv5jZCbWMZ0yFIXH5NeiVDYlTi7uIiEg4ZbKOrINoJJegu5EVdK03Ft8zm54BIGr6f0JE6qtmCbqZecDVwPHAYuAsM1s84rB/AX7knDsYWAl8q1bxjGvkFPfIzg2JU4IuIiISLvnOuHyL+7AdV7IpVdCloDXeCoCZBRyJiEw3taygHwqsds694JxLAjcDp4w4xgFtufvtwKs1jGdshQQ939JU2ZC4whr0rNagi4iIhEl+tkw0UiJBz6S1Bl0KMi7DnKY5QYchItNQLft25gEvFz3uBg4bccxlwF1m9jGgGTi6hvGMbcSQuIq3WYvk1qBntAZdREQkTPJL1/L7oA+TTQ1drJdpL+uyeOYFHYaITEO1rKCX6gkaWZI+C7jBOTcfOAH4P2a2Q0xmdoGZrTKzVevXr69BqBQNictNcY9UtgZdLe4iIiLhlL/wXqigF38tySQhdw4XybgMkR2/koqI1Fwtf/N0A7sVPZ7Pji3sHwJ+BOCc+xPQAHSO/CDn3LXOueXOueVdXV21iXbEGvSYZ6S1zZqIiMiUkT+v59egD5NVi7sMyWazeBFV0EWk/mqZoD8E7GNmC80sjmXkSFMAACAASURBVD8E7vYRx7wEvB3AzBbhJ+g1KpGPo9SQuJ3YZk0VdBERkXBJj1yDPrKxT0PiJEcVdBEJSs1+8zjn0sCFwJ3AU/jT2p8ws8vN7OTcYf8L+LCZ/Rm4CTjXDZvYUkdVGhKXT9BVQRcRkalgUm2ZOo78hff8Nms7fOXQNmuSozXoIhKUmp6JnHN3AHeMeO5zRfefBI6oZQwTVhgSl9sHvcJt1tTiLiIiU0XRlqnH4C9de8jMbs+dv/PyW6Z+O7ed6h3AgroHOwFjtriDKuhSoAq6iARFv3ny8r+Ec9PXo97ODYlLZTXFXUREJr3JtWXqOHoHhpaxlaQ16JKjCrqIBEUJep6Zf+W8sAZdQ+JERGTaK7Vl6rwRx1wGvM/MuvGr5x8r9UF12ZFlHJv6krlYRjlA26xJjiroIhIU/eYpFokW1qBHvZ0bEqcEXUREpoCqbZlalx1ZxpHJdcbNn9HoxzTyr6Jt1iRHFXQRCYoS9GKRaGENeswzUhVU0OMRTXEXEZEpo2pbpoZBMnfhPeapxV3Gpgq6iARFv3mKedGd3mbNzIhH4qqgi4jIVDC5tkwdRyp3Xo9HR5nirhZ3ycm6rBJ0EQmEfvMUi0R3ekgc+OvQVUEXEZHJbtJtmTqOwXQuQVcFXcaRyWbwImpxF5H606XiYkVD4mJehFQF26yBvw5dCbqIiEwFk2rL1HEkcwl6Il9BH7kGXdusSU7WZYmZ/n8QkfpTBb1YZKjF3YsYmQrWoIOfoKvFXUREJFzyCXq8RILuQBV0KdCQOBEJihL0YhFvqIIeMVIZt+P6tAlQi7uIiEj4pMYYEpcFJehSoCFxIhIU/eYp5hXtg547eVdSRVcFXUREJCS2Dg2df61nABiqoBd3uDtQi7sUqIIuIkFRgl5sxJA4gHQFCXrCS5DMqoIuIiISqJcegP9YBI/dBMDP/+In69HIjtu7+wm6EjLxqYIuIkHRb55ixfugR/x/mlQFW61pSJyIiEgIrHvCv33pTwA0xaMctFsHZn6CXrwGPWtA27x6RyghpQq6iARFCXqxSBSyIyroFWy1pn3QRUREwmB4pTydybLP7JaSR2YxiCbqEZRMAqqgi0hQ9JunWPEa9MhOtrirgi4iIhIS/rk8mXHEokNffYoHwfpT3ON1jkvCShV0EQmKEvRikShkhg+JS1ewF7qGxImIiIRAvgKaS8ST6QzxognuO2yzpgq65GSyGSIRfU0WkfrTb55iRdusFSroFbS4q4IuIiISApZvcffP5amMI+btOCAO8tusqYIuPlXQRSQoStCLRYZa3PN7pGpInIiIyCSXu9aeymSH7YE+bEhcxNMUdynQGnQRCYp+8xQrNSSu0n3Qs2pxFxERCdZQtTybdaSzbliCXsypvV2KqIIuIkFRgl7MixW2WYvuxDZranEXEREJE0cqN1MmPsqQuKza26WIKugiEhT95ikW8SCTq6Dn1qBnKq2ga0iciIhIfbhRztU2VEFP5WbKjLYG3SkZm1Te87P3cPPTN9fks3sGe9g0sEkJuogEQr95ikWiQ0PicifwVIVD4rIuSzr3WSIiIhIg50il/Qr6qC3uWn8+qTy16Sm+9MCXavLZr/S+AsCcpjk1+XwRkbEoQS9WYkhcusIWd0Bt7iIiIoEqrqCPnaBn5y2rS0QSfvkuyCWzlgQciYhMR0rQixVX0COVD4mLRWIAanMXERGph3Fb3B3JXII+2j7oWQ2Jk5yB9AAACf0/ISIBUIJezCtucd+5IXGgBF1ERCRYuQTdOZL5FvfoUFW9eEgcSsYkZ1tyGwANXkPAkYjIdKQEvVgkWhgSlx8ik65gDXo8NwlWLe4iIiL1MH4F/dUtflV0tNky2bQuqovvsfWPAdDR0BFwJCIyHSlBLxbZcZu1dFZr0EVERCY15wrt7AtmNQ89Xdzi3r573cOScIpGogDMa5kXcCQiMh0pQS8W8SDrV9Dj0Z2b4g5qcRcREamL0daglxgSl4iOMsU90VLtqGSSGkwP0hpvDToMEZmmlKAXGzYkbifWoOfWsQ1kBqoXm4iIiFTIkUz7SXzUK70GPatt1iRnMDOo9eciEhgl6MW82A77oFeyBj3/S70/3V+92ERERKQ8NpSM55esxUfbBz0ar0tIEn5Pb3q6ME9IRKTelKAXi0TBZSGbLZzAUxWsQW+MNgJK0EVEROpjnIvpzhU64qKjbbOWW3cskvAShUnuIiL1pgS9WP7knE0PbbOWLj9Bb4o2AUrQRUREAmX5rzmOVK7FPVbU4l4sowRdcgYzgxzQdUDQYYjINKUEvVghQU8Ntbhny29xb4zlKugpJegiIiI1N+qQuKHXU+O0uGe0Bl1ytAZdRIKkBL1YUQW90OJewRp0tbiLiIiES74jbliLe1FirwRd8gbSA4WBvyIi9aYEvZgX82+zGaKR/DZr5be4a0iciIhIPY1yMb0wJM4VLrgPa3G/+0uFu2pxl7zu3m5ikVjQYYjINKUEvVj+6nkmhRfJT3EvP0H3Ih4JL6EEXUREJCTyLe6x4gr6648X7mc796l7TBI+mWwGGN5dISJST0rQi+WvlmbTmBlxL0KyghZ38Nvc+9J9VQxOREREypOrlrviIXFFX33SycLdjKcKukAqmwJgQfuCYAMRkWlLCXqxoiFx4O+FXkkFHfwEXRV0ERGROhi32ulIZ7NEjEKHHM7hMoOFI/KVU5nekln/ok08on3QRSQYStCLFa1BB4hGrKIp7qAEXUREwsPMLjSzGUHHUXc2tN48mckOVc+7H4ZNLww7NOOUoE8G21PbWbt9bc0+P5nJJeieEnQRCYb6uYoVrUEHiEcjJFVBFxGRyW8X4CEzewS4HrjTTalFtqP9VYa3uMe8CKx/Bq57m/90w9Ck7qyr7Hwv9XX2L87mhZ4Xxj+wQqn8d0Al6CISEFXQixVtswYQjUTU4i4iIpOec+5fgH2A7wDnAs+Z2ZfNbK9AA6u1oinu6WzWn+Det7HwsrOhrdVUQZ8capmcw9AadE1xF5GgKEEvVjQkDiAWNdI7MSROCbqIiIRFrmK+NveTBmYAPzazfx3tPWZ2nJk9Y2arzeySEq9fZWaP5X6eNbMtNfsLjGXUZoCiCnom6++Bnio6N8/YvXBXa9AFhlrcY54SdBEJhlrci42ooMcianEXEZHJz8wuAs4BNgDXAf/knEuZWQR4DvjnEu/xgKuBY4Bu/Bb5251zT+aPcc59ouj4jwEH1/QvshOSaUfci0DxubmoSqoKuoCGxIlI8GpaQR/vynvumPeY2ZNm9oSZ/bCW8YzLG9Hi7lVeQW+ONbM9tb1akYmIiOyMTuBdzrl3OOducc6lAJxzWeCdo7znUGC1c+4F51wSuBk4ZYw/4yzgpmoGvdOKhsQ98tJmf4L7608Mva4EXUbo3tYNaA26iASnZgl60ZX344HFwFlmtnjEMfsAnwaOcM4tAf6xVvFMSL6CnhsQEvMipLOVVdBb461sS26rVmQiIiI74w5gU/6BmbWa2WEAzrmnRnnPPODlosfdued2YGZ7AAuB340WgJldYGarzGzV+vXrywx/PONfTI8YbB1IweDWoXcV7X2uIXECsKF/AwCdjZ0BRyIi01UtK+gTufL+YeBq59xmAOfcuhrGM76RQ+K8CMkKK+ht8Tb60/2FYSMiIiIB+jbQW/R4e+65sViJ50Y7Ka4Efuzc6GVo59y1zrnlzrnlXV1d4/zR1TK0Bj2TdRy5bxekh/Y+L66gp3Pnfpne8mvQ57fMDzgSEZmuapmgT+TK+77AvmZ2n5ndb2bHlfqg2l51LxIZvg96LGIVT3FvjbcCqIouIiJhYMXbquVa28ebQ9MN7Fb0eD7w6ijHriTI9vZxd4xzpDK5bdaKhsSpgi4jDWQGAEhEE+McKSJSG7VM0Cdy5T2Kv+3LCvy1a9eZWccOb6rXVff8PujZoRb3lBJ0ERGZ/F4ws4vMLJb7+Tgw3n5VDwH7mNlCM4vjJ+G3jzzIzN6APxH+T1WPemdZ7muOcyQzWT9BTw8UXnaRoQRda9AFYDAziGeetlkTkcDUcor7RK68dwP354bVvGhmz+An7A/VMK7RecO3WYt6Rn+q8hZ3UIIuIiKh8PfAN4B/wb9Y/lvggrHe4JxLm9mFwJ2AB1zvnHvCzC4HVjnn8sn6WcDNxRX6+hv+RzvnsN99Ef5wpf9Ez8v8KnUebX/tx99hLseG6hRK0MNnTc8a3v/L97NlcPTd+5beuJR/Wv5PfGDJB7j56Zv50gNfAiDhVVYBT2fTNEYbK3qviEg11DJBL1x5B17Bv/J+9ohjfoJ/Yr/BzDrxW97Hu6JfO1UcEteW8BP0rUXDaERERIKQm/GysoL33YE/YK74uc+NeHzZTgVXA86B5ZNzgPXPMIsUj884lv0XLYKZe8GWl3CzF8Aj/jbw2gc9fNZsXTNmcp73w6d/yAeWfKCQnAOcvWjkV86J22/GfhW/V0RkZ00oQTezvYBu59ygma0ADgC+55wb9bfmBK+83wkca2ZPAhn8fVk37txfaSdEG/zbXPtbzDNS6coKAq0xv8V9a0oJuoiIBMvMGoAPAUuAhvzzzrnzAguqhnY4c+eWrj089yz2P+ZdQ89331u4qyFx4ZMf2FaJi5ddXMVIRETqZ6Jr0G8FMma2N/Ad/K1Uxt2z3Dl3h3NuX+fcXs65L+We+1y+Lc75LnbOLXbOLXXO3Vzh36M6Yk3+baoP8Ke4p3ZimzVQi7uIiITC/wF2Ad4B3IO/7GzqnKDcji3uJUUbSj+PWtzDaDAzOP5BIiJTzERb3LO5ivhpwNecc/9pZo/WMrBAxHJrjnITXuNehHSF26wpQRcRkRDZ2zn3bjM7xTl3o5n9EL+LbUoa7cwdiY2eoL+2/TU29m9kVuOs2gQlO8hkMzyw9gEGigb3Ffvz+j9P6HM2D2zmdy/9rpqhiYgEZqIJesrMzgLOAU7KPTf1xlvmK+jJXAU9YhVPcW+MNhKNROkZ7KlWdCIiIpVK5W63mNn+wFpgQXDhVNvELqanEzOGv6uo0v6Dp37AD576AX89569VjUxG98DaB/jIrz+y05/Tl+7j43d/vPBYE9hFZDKbaIL+QfwJsF9yzr2YG/z2/dqFFRAvCl58eIt7hRV0M2NWwyw29G+oZoQiIiKVuNbMZuBPcb8daAE+G2xItVOqw/1114FrGL6Tq5tgYi+1kS9iXLXiKua1zCt5jBfxOP320wuP5zbP5ZaTb+GIm44A4L+O/S/a4+2A/98zlU2xeNbiGkcuIlI7E0rQnXNPAhcB5E7wrc65K2oZWGBijUUt7lbxFHeArsYuJegiIhIoM4sAW51zm4F7gT0DDqn6Rq5BL5F4v+ZmEotOdPSO1EN+jfmiWYtGTdBH2nvG3oWtbAH27tibzsbOmsQnIhKECZ2pzOz3ZtZmZjOBPwPfNbP/qG1oAYk1Q2q7f9eLkExXnqB3NnWyvn99tSITEREpm3MuC1wYdBz1VKqCPkicuGcjjlMFPUiDaT9Br3TPclA7u4hMPRNtcW93zm01s/OB7zrnLjWzv9QysMAUVdATsZ1L0Lsau/jzuokNOBEREamhX5vZJ4H/Brbnn3TObQoupCrqfb1w9+uxb/LXb1zHISMOeTYe4fsv/iNffPI5Ljr4Is5fen7Jj/rne/+5hoFKsTU9a4CdS9A986oUjYhIOEw0QY+a2a7Ae4D/XcN4ghdrGkrQox7prCOdyRL1ym+L62rsYvPgZlKZFDFPV3hFRCQw+f3O/6HoOcdUaXf/4zcKd5faC8R6h5+ze2nmX3frhf7nAPjGo9/gnXu+s9AKv3fH3qzeshqAJzc+WaegBeCIeUfQHGse85gLDriAJzY8wet9r3P8wuMLzz2+4XGa8gN+RUSmiIkm6Jfjb8dyn3PuITPbE3iudmEFKN4ESb+40BDzT/AD6SwtFSTonU3+mqiNAxvZpXmX6sUoIiJSBufcwqBjqKkBf9jY09ndOC75VdZcceKwl1sAblw67Ll0Nl24/+U3f5lFsxbVOkqp0McO/tiEnhMRmQomOiTuFuCWoscvAKeP/o5JLNZY2GatIea3TQ2mMrQkJnotY8icpjkArN2+Vgm6iIgExsw+UOp559z36h1LbfhryyNMfFlayqU0xV1EREJnokPi5pvZbWa2zsxeN7NbzWx+rYMLRKypsM1aIjpUQa/EHm17APC3rX+rTmwiIiKVOaTo5y3AZcDJQQZUVeYn6DbOYcUy2UzR28t5p4iISO1MtCz8XeCHwLtzj9+Xe+6YWgQVqEQbDGwFhiroA6nMWO8Y1dyWuUQtqgRdREQC5Zwb1g9sZu3A/wkonBooP8HOuAwqoIuISNhMdGF1l3Puu865dO7nBqCrhnEFp2km9PtDbRPRfIt7ZRX0WCTGvNZ5rNm6plrRiYiIVEMfsE/QQVRNoYI+8YzbOVdocbcKEnwREZFamGgFfYOZvQ+4Kff4LGBjbUIKWONMSPZCOkmiMCSusgo6+G3uL/a8WK3oREREymZmP2OoXhwBFgM/Ci6iais/QRcREQmjiSbo5wHfBK7CP8H/EfhgrYIKVNMM/7Z/Ew1Rf2u0SlvcARbNXMT/vPI/9KX6tBWIiIgE5cqi+2ngb8657qCCqbpKKug4DYkTEZHQmVCLu3PuJefcyc65LufcbOfcqcC7ahxbMBpn+rd9mwrbrA1WOCQO4ICuA8i6LE9sfKIa0YmIiFTiJeAB59w9zrn7gI1mtiDYkKrI/PN1OQn681ueH3q7hsSJiEhIlL+595CLqxZFmDTlEvT+TcO2WavUAZ0HAPDoukd3OjQREZEK3QLD9iDLULR96uRX/hT3z/zPZ3BOFXQREQmXnUnQp+bl5qZZ/u32DUPbrFU4JA6go6GDxbMWc8/L91QjOhERkUpEnXPJ/IPc/XiA8dREuWvQ8y3ukZ36OiQiIlI9O3NGmpqXndt38297Xh6qoO/EkDiAt+/+dv6y4S+8vv31nY1ORESkEuvNrLDvuZmdAmwIMJ7qqmANOlCooKvFXUREwmLMBN3MtpnZ1hI/24C5dYqxvho7oKEDNq8p2ge98go6wHELjsMwbnl2CnUTiojIZPL3wGfM7CUzewn4FPCRgGOqnsIa9PIUtllTgi4iIiEx5hR351xrvQIJlRkLYPMamuJ+gr49md6pj9u9bXdW7LaC/37mvzlnyTm0xqfnP6uIiATDOfc88Hdm1gKYc25b0DFVV66CbuVV0LMum3u3EnQREQkHLboqZdbe8PqTJDwj5hnbBnYuQQf4+wP/nq3JrVz18FVVCFBERGTizOzLZtbhnOt1zm0zsxlm9sWg46qaogr4frtM/CJ4YQ266euQiIiEg85Ipex2GGx7FdvaTUsiSm8VEvTFsxbzgcUf4JZnb+G2526rQpAiIiITdrxzbkv+gXNuM3BCgPFU2dAa9DOWzZ/wuwpr0FVBFxGRkFCCXsqCI/zbZ++ktSHGtoFUVT72ojdexJvmvolL/3gpNzx+g7Z3ERGRevHMLJF/YGaNQGKM4ycXy99UNsVda9BFRCQslKCXMnsxzFkKq75Le6I6Le4AsUiMrx31NY7e42j+/eF/57w7z+OJDU9U5bNFRETG8H3gt2b2ITP7EPBr4MaAY6qi8vdBB61BFxGR8FGCXooZvPkfYd0TnJ2+rWoJOkBjtJErj7ySSw+/lGc3P8vKX6zkg7/6ILc+eyubBzZX7c8RERHJc879K/BFYBGwGPgVsEegQVVTrgIeobxdV/KdbFqDLiIiYTHmFPdpbf/T4elfcNYTN9CSXA99b4CmmVX56IhFOGPfMzhuwXH8+Nkfc8uzt3DZny7j83/6PPvM2Idlc5axeNZi9mrfi7069qIp1lSVP1dERKa1tUAWeA/wInBrsOFUU2UV9EKLuyroIiISEkrQR2MG77qWe9ZGOXHjLXDVEjjovXDI+TB7v6r8ES3xFs7d/1zOWXIOT296mnu67+GR1x/hJ6t/wk1P31Q4bnbjbHZp3oU5zXOY0+T/dDR00B5vpz3RTkeig7ZEG+3xdmJerCqxiYjI5Gdm+wIrgbOAjcB/42+zdlSggVWbDQ2JK0dhSJzWoIuISEgoQR+LF+OPe1/MV9cewi8OfAR75Hvw0H/BgrfAoRfAG04Ab+f/Cc2MRbMWsWjWIgAy2Qzdvd2s3rKa57c8z8vbXmbt9rWs3rKa/3nlf+hP94/6WdFIlMZoI43RRpqiTYX7+Z9ENEEsEiv8xL340GMvVvK+F/HwzCNikWG3oz0fscjQ/ciOr0UjUf8YIiVf98zTlyURkep4GvgDcJJzbjWAmX0i2JBqIX/OcGWdP7JoDbqIiISLEvRxdDYneDIzj63HfZD2Y78Ij3wPVl0PP3o/tM2DE/8d3nB8Vf9ML+KxR9se7NG2B2/f/e3DXnPO0ZvqZcvgFrYObqVnsIeeZE/hcX+6v+TP1uRWXu97ncHMIKlsimQmSSqbIp1Nk8qkSLvqrbOvBsOGEvbIiKS/KIkveYFglKR/2AWE3DFRi9Ica6Y13kpLvIW2eBstsRZa4620Jdpoi+d+Ev7zWqcoIpPM6fgV9LvN7FfAzZTfCR5+ud/NZbe4aw26iIiEjBL0cXS2xgHY2DtIe1cnvOViOOLj8Oyd8Psvw00rYeUPYb8T6xKPmdEab6U13gqt1fvcrMuSyqZIZVL+bVESn8lmyLgMWZcl67KF++lsetjjkrfZ7A7vzX+ew5HJjvHeEp9RKo4d7o/xmclscthnprIptqe205vspTfVW1iPWErEIrTEWmhPtA9L3IvvL2hbwMGzD2ZGw4zq/ccREamQc+424DYzawZOBT4BzDGzbwO3OefuCjTAamn39z7/Webwst6mFncREQkbJejj6Gzxt4nd0Jtkz67ckxEP9jsB9joKvnMs/OKTsPfREJ28W8pGLELCS5DwJu/fYWdlXZa+VB+9qV62Jrf6HQrJHrYObvUf554r3E9u5bXtrxWez3chRC3K2YvO5uJlF+NFvID/ViIi4JzbDvwA+IGZzQTeDVwCTI0EvWU2AN/JnMB5Zbyt+Pe2iIhIGOiMNI5ZzfkEfXDHF2ON8PbPwQ/OgOfugkUn1Tk6qaaIRWiJt9ASb2GX5l3Keq9zjr50H89ufpafrP4J33vye8xtmct7F723RtGKiFTGObcJ+P9zP2Mys+OArwMecJ1z7ooSx7wHuAxwwJ+dc2dXNeAyuPJmxJHO5hL0iL4OiYhIOGjR1TiKW9xL2nMFJNrh2V/VLSYJHzOjOdbMwbMP5rLDL2PZnGX88KkfFtonRUQmGzPzgKuB4/H3Tj/LzBaPOGYf4NPAEc65JcA/1j1QKD8zz8kn6Op2EhGRsFCCPo6ZTXHMYH1vsvQBXgwWHAEvPVDfwCS0zIxj9jiGl7a9xNrta4MOR0SkUocCq51zLzjnkvgD5k4ZccyHgaudc5sBnHPr6hzjMA6jnOXkP1n9E0AVdBERCQ8l6OOIehFmNMVLt7jnzX0jbHwOBrbWLzAJtSWzlgDw9KanA45ERKRi84CXix53554rti+wr5ndZ2b351riSzKzC8xslZmtWr9+fQ3CLd/G/o2csPAErUEXEZHQUII+AZ0tcdZvGyNBn72ff7v5xfoEJKG374x9AVi9ZXXAkYiIVKxULXpkL3kU2AdYAZwFXGdmHaU+zDl3rXNuuXNueVdXV6lDdkJlLe5vmvcmvvrWr2qKu4iIhIYS9AnYtb2R13r6Rz+gfTf/dstL9QlIQq8p1sTMhpm80vtK0KGIiFSqG9it6PF84NUSx/zUOZdyzr0IPIOfsE8K8Ug86BBERESGUYI+AXM7Gnhty8DoB3Ts7t8qQZcic5vn8tr214IOQ0SkUg8B+5jZQjOLAyuB20cc8xPgKAAz68RveX+hrlEWcVjJsv9oYl6sZrGIiIhUQgn6BMxtb2Tj9iQDqUzpAxpnQKwZerrrG5iE2q4tu/Jq78hik4jI5OCcSwMXAncCTwE/cs49YWaXm9nJucPuBDaa2ZPA3cA/Oec2BhNx+WIRJegiIhIumooyAXM7GgFY2zPAgs7mHQ8wg5bZ0Bvo8FoJmbnNc7m3+16cc1rfKCKTknPuDuCOEc99rui+Ay7O/QSnwm3W4p5a3EVEJFxUQZ+AXTsaAHh1yxjr0FtmQ+/rdYpIJoOupi4GM4NsTWq6v4hIPTgo64Ko1qCLiEjYKEGfgLntfgX9tZ4x1qG3zIbt4dg2RsKhq9GfUryhf0PAkYiISClagy4iImFT0wTdzI4zs2fMbLWZXTLGcWeYmTOz5bWMp1K7tPsV9LVbx0rQ56iCLsN0NfkJ+vp+XbgREamtsVvcn9j4RMnnVUEXEZGwqVmCbmYecDVwPLAYOMvMFpc4rhW4CHigVrHsrIaYx8zm+Ngt7s2zoX8zpJP1C0xCLV9BX9+nBF1EpB4cRqkO95U/X1ny+GhEo3hERCRcallBPxRY7Zx7wTmXBG4GTilx3BeAfwXGKE8Hb5e2hvFb3EFt7lKgCrqISLhFTCv9REQkXGp5ZpoHvFz0uDv3XIGZHQzs5pz7+VgfZGYXmNkqM1u1fn0wyc7cjgkm6Gpzl5zmWDON0UZV0EVEaq3CKe7l7ZouIiJSe7VM0Eud9QpnUDOLAFcB/2u8D3LOXeucW+6cW97V1VXFECdul/YGXusZa4r7HP9WFXQpMrtptobEiYiElCroIiISNrU8M3UDuxU9ng+8WvS4Fdgf+L2ZrQH+Drg9rIPidm1vZEtfiv5kpvQBqqBLCZ2NnazrWxd0GCIi04IrsyZezpZsIiIi9VDLBP0hYB8zW2hmcWAlcHv+Redcj3Ou0zm3wDm3ALgfONk5t6qGMVVsl7ZxJrk35xN0JWMypKuxSxV0EZGQUou7iIiETc0SdOdcGrgQuBN4CviRc+4JM7vczE6u1Z9bK7PbEgCs3zZY+oBYAyTa94uilAAAIABJREFUlaDLMF1NXazvX4+rcH2kiIhMRIVr0FVBFxGRkKnp/iLOuTuAO0Y897lRjl1Ry1h2VmfLOAk6QNMM6N9Up4hkMuhq7KI/3c/21HZa4i1BhyMiMqU5oOQ+a6OI1LSRUEREpHw6M01QV6ufoG/oHSNBb+iA/i11ikgmg87GTkBbrYmIhJEq6CIiEjZK0CdoRlMcL2JjV9AbO2BACboMmd3kzybQOnQRkRqqdJs1JegiIhIyStAnyIsYM5vjqqBLWboa/W0BNcldRKQeypziriFxIiISMkrQy9DVklAFXcrS2eS3uKuCLiISPtoHXUREwkZnpjJ0tiYmVkHXxG7JaY21EovE2DiwMehQRESmsApb3FVBFxGRkFGCXoYJVdCzKUj11S8oCTUzoy3extbBrUGHIiIiI2gNuoiIhI0S9DJ0tsbZ0JscfU/rhg7/dqCnfkFJ6LUl2tiaVIIuIlJrDitnlzVV0EVEJHSUoJdhRlOcZCZLXzJT+oBEq3872Fu/oCT02uJK0EVEwkhr0EVEJGx0ZirDjKYYAJv7kqUPKCTo2+oUkUwG7Yl2tbiLiNSSZr+IiMgUoQS9DB1NcQC29KVKH5BP0JNK0GWIKugiIvXhKK9tXRV0EREJG52ZyjAjl6Crgi7l0JA4EZFw0hp0EREJGyXoZRhqcR+lgh5v8W+VoEuRtkQb21LbyGRHmV0gIiKBUAVdRETCRmemMgy1uI9WQW/zb5WgS5H2eDsAvSkNDxQRqaWyp7hrmzUREQkZJehl6MhX0LePtgZdFXTZUVvuwo3a3EVEwkUt7iIiEjZK0MsQ8yK0JqKjr0GPJsBLKEGXYdrifoLek+wJOBIRkSmqwinuanEXEZGw0ZmpTB3NsdFb3MGvoitBlyLtCb/FXRV0EZHa8qe4T5wq6CIiEjZK0Ms0oyk++pA48Ce5K0GXIvk16Kqgi4iEi9agi4hI2ChBL1NHU3ycCnorJDUMTIZoDbqISK1V1uKuCrqIiISNEvQyzWiKjV1Bj6uCLsNpDbqISDhpDbqIiISNzkxlam+MsXVgrBZ3rUGX4eJenMZoIz2DStBFRGpJ26yJiMhkpwS9TG0NMbb2p8hmR2mn0xp0KaE90a4EXUQkZNTiLiIiYaMEvUztjTGyDrYn06UPiLdoDbrsoC3ephZ3EZFaqXCbNVXQRUQkbJSgl6mtMQpAT/8obe6JVhhUgi7DtSfaNSRORCYdMzvOzJ4xs9VmdkmJ1881s/Vm9lju5/wg4sxzWFlV8Yi+BomISMhEgw5gsmlvjAGwtT8NM0ocEG+B1HbIZiGiE7/42uPtrNm6JugwREQmzMw84GrgGKAbeMjMbnfOPTni0P92zl1Y9wCrQQV0EREJGWWQZWpr8BP00SvoLf6t2tyliNagi8gkdCiw2jn3gnMuCdwMnBJwTKOorMVdU9xFRCRsdGYqU1u+gj7aJPdEq3+rBF2KtMXb6BnswVW4TlJEJADz/l97dx4l13UXePz7q6reF8mSWk5bki0vcmKT1VEUhxAIHk+wnRwrCQ4xhJMMyzFkMJmQCcEhTIBMcg7Ec5gBxpNgICThAAYcDCI4iExWw8SO5NiWrXhTHBvJkqz22tpbrb7zR71ulbqrSurq6lq6vx+fcr13363Xv7rdrdu/uvfdB+ws2d+VlU334xGxLSJujYg1lU4WEddFxNaI2DoyMlLvWIEsTZ/NKu4OoUuSWowJ+ixNTnGvOILemY2gu5K7Sgx2DTI2McaR40eaHYokna5y2ev0Txn/EVibUno58H+Bz1U6WUrp5pTS+pTS+qGhoTqGWTtH0CVJrcaeaZamRtCrLRIHLhSnkyzpWgLgQnGS2skuoHREfDWwu7RCSumZlNLRbPePgVc3KLaTOTtJkrRAmKDP0kBXgYgqCfrkCPqYI+g6YUlnMUH3VmuS2sgWYF1EnBsRncC1wKbSChExXLJ7NfBgA+ObM0fQJUmtxlXcZymXCwa6CqdeJM4RdJUY7BoEcKE4SW0jpTQeEdcDm4E88JmU0vaI+BiwNaW0CXhfRFwNjAPPAv+paQEDs72q3GvQJUmtxgS9BoM9HYweGS9/sNNV3DXT5Ai6U9wltZOU0u3A7dPKPlqy/WHgw42Oq14cQZcktRp7phos6emoMoJeHCl1kTiVmroGfcwEXZLqr/I16N49Q5LUTkzQazDY3VFlkThXcddMkwn680efb3IkkrRwJYKI05+27gi6JKnV2DPVoOoIeqEbIu8Ud52kt9BLd76bkcPzc+9fSdLseQ26JKnVmKDXYLCnwOiRCgl6RHEU3UXiVCIiWNm7kpFDJuiSVHc1TmN3BF2S1GrsmWpQdQQdoHPAEXTNsLJ3JfsO7Wt2GJK0YCWYMSaeqlyf7gC6JKnVmKDXYLC7gyPHJjg6frx8ha5+r0HXDCt7V/LUoaeaHYYkKZPzzyBJUouxZ6rBkt4OAEYPV7jVWteACbpmOLP3TEYOjbiisCTVXW3/rs5mQTlJkhrBBL0Gg93FBL3iNPfOfqe4a4aVvSsZmxjjhaMvNDsUSRIuEidJaj0m6DUY7CkAcOBopRF0F4nTTEO9QwBOc5ekeVK8zVqzo5AkqXYm6DXo7yqOoB84UiFBd5E4lXFm75kALhQnSQ1U7bIiV3GXJLWaee2ZIuKKiHg4InZExA1ljn8gIr4bEdsi4isRcc58xlMv/V2TI+gVpri7SJzKmEzQHUGXpDqrcW0Pp7hLklrNvCXoEZEHbgKuBC4GfjIiLp5W7R5gfUrp5cCtwCfnK556GuguJuj7K46gZwm6i4GpxFDvELnIsffg3maHIkkL0mynuLtInCSp1cznCPoGYEdK6bGU0hhwC7CxtEJK6WsppUPZ7p3A6nmMp25OjKBXWcU9HYfxIw2MSq2ukCuwsnclew7uaXYokiQcQZcktZ75TNBXATtL9ndlZZX8HPClcgci4rqI2BoRW0dGRuoYYm36uk4xgt41UHx2oThNM9w3bIIuSXVX44w183NJUouZzwS9XLdXtgeNiJ8G1gM3ljueUro5pbQ+pbR+aGiojiHWprOQo6uQqzyC3tlffB7zOnSdbLhvmD0HTNAlaT4kZo6KpyrJuyPokqRWM58J+i5gTcn+amD39EoRcTnwEeDqlNLReYynrga6C1VG0LME3RF0TTPcN8zeQ3uZSBPNDkWSFj0TdElSq5nPBH0LsC4izo2ITuBaYFNphYh4FfBHFJPztrr3VH9X4TRG0E3QdbLhvmHGJ8Z5+vDTzQ5FkhaOWldxd5E4SVKLmbcEPaU0DlwPbAYeBP4mpbQ9Ij4WEVdn1W4E+oG/jYh7I2JThdO1nIHuDg4cqXSbtcHis7da0zTD/cMAXocuSS3AEXRJUqspzOfJU0q3A7dPK/toyfbl8/n151PVEfSpKe4m6DrZcN+JBP0VQ69ocjSStLDM+jZrJuiSpBYzn1PcF7T+ategO8VdFUwm6HsPeC90SWqEaovEmZ9LklqNCXqNBk5rBN0EXSfr7+xnoGOA3QdnrJcoSapZjdegm6FLklqMCXqN+rtdJE61eVH/i7wGXZLmQZplwu0icZKkVmOCXqP+rgIHjoyTyq0cm8tDR6/XoKus4b5h9h50irskNZsj6JKkVmOCXqP+7gLjE4mj4xXuZ901YIKusob7htl9wCnuklQ3Nd5mLRf+GSRJai32TDUa6CougF9xobjuJXDk+QZGpHYx3DfM6NgoB48dbHYokrTgzJi2XlvuLklSU5ig16i/u5igV7wOvXcFHHymgRGpXUyt5O40d0lqKqe4S5JajQl6jfq7OgDYf+RY+Qp9y+HQ0w2MSO1iuP/EvdAlSfVQ4yruLhInSWoxJug16s+muB+oNMW9dwUcNEHXTJMj6F6HLknN5Qi6JKnVmKDXaCCb4r6/0hT3vhVw+FmYqLCInBatlb0r6cx1snP/zmaHIkkLSpplyu0IuiSp1Zig1+i0RtDThAvFaYZc5FgzsIYnRp9odiiStDBUWcU9uUqcJKmNmKDXaGoEvdI16L3Li89Oc1cZZw+ezb+P/nuzw5CkRc0p7pKkVmOCXqOB7slF4ipNcc8SdBeKUxnnDJ7Dzv07mUheAiFJ9ZIIZjNrfbBzcP6CkSSpBiboNeos5OjuyDFacQR9RfHZEXSVcfbg2YxNjHmrNUlqkqVdS+nIdzQ7DEmSTmKCPgeD3R1VRtCzBN0RdJVxzsA5AF6HLqnlRcQVEfFwROyIiBuq1LsmIlJErG9kfEWzv87ca9MlSa3IBH0OBroLlUfQ+4aAgAP7GhqT2sPZg2cDJuiSWltE5IGbgCuBi4GfjIiLy9QbAN4H3NXYCE+WmHlduYm4JKmdmKDPwWBPB6OHK4yg5zugfyWMPtnYoNQWzuw9k76OPnY8v6PZoUhSNRuAHSmlx1JKY8AtwMYy9f478EngSCODkyRpoTFBn4PiFPcKI+gAg2fB6O7GBaS2ERG8+IwX89CzDzU7FEmqZhWws2R/V1Y2JSJeBaxJKX2x2oki4rqI2BoRW0dGRuobZQ2D5KnKrdkkSWoWE/Q5GOzpYLTSNegAg6tM0FXRRcsv4pHnHuH4xPFmhyJJlZRbE30qs42IHPA/gf96qhOllG5OKa1PKa0fGhqqY4iSJC0cJuhzMNBdYPRwlRH0gWGnuKuii5ZdxOHxwzyx3+vQJbWsXcCakv3VQOknzwPAS4GvR8TjwKXApuYsFFf+NmuOlEuS2okJ+hxMruJesfMfPAuOvABHDzQ2MLWFi5cX11m6f+T+JkciSRVtAdZFxLkR0QlcC2yaPJhSeiGltCKltDaltBa4E7g6pbS1sWG6irskaWEwQZ+DwZ4CY8cnODo+UaFCdpne/j2NC0pt4/yl57Osexl37WnqoseSVFFKaRy4HtgMPAj8TUppe0R8LCKubm50c/ODZ/1gs0OQJGmGQrMDaGcD3R0AjB4+RndHfmaFwbOKz6NPwop1DYxM7SAXOTa8aAN37rmTlBIxfV6mJLWAlNLtwO3Tyj5aoe4bGxFTJWnGTdYq+8QPfWJeY5EkqRaOoM/BYHfx842KC8UtyUbQn99Z/rgWvded9TpGDo+4mrskNVhXvqvZIUiSNIMJ+hwM9mQj6JVutbbkbMh1wDPe61rl/eiaH6UQBb70/S81OxRJal9VFoLzWnNJUjsxQZ+DqRH0Siu55wuw7DwTdFV0RvcZvH7V67n9+7d7uzVJmqMEM1ZxlySpnZigz8EZvZ0APHdorHKlFevg6UcbFJHa0cYLNvLUoaf42s6vNTsUSZIkSU1kgj4HKwe7Adg3erRypeUXwLOPwfEK16lr0btszWWs6l/FZ7d/ttmhSFKbchq7JGlhMEGfg77OPD0defbtr5KgD70EJo7BM46iq7x8Ls97fuA93DdyH9/c9c1mhyNJkiSpSUzQ5yAiWDnYVT1BX/Xq4vOTdzcmKLWla9Zdw9rBtdy45UaOHa+wpoEkqapEwLQbraUqC8hJktRqTNDnaOVAF/tGj1SusPwC6FoCu7Y2Lii1nY58Bx96zYd4fPRx/vDeP2x2OJLUXkzCJUkLhAn6HK0c6Gak2gh6LgerLoGd325cUGpLb1j9Bn7iwp/gzx74M6e6S5IkSYuQCfocrV7Ww67nDnN8osqn9+f/KOzbDi882bjA1JZ+9TW/ykuWvYQPfuOD3D9yf7PDkaQ2E95mTZLU1kzQ5+j8oX7Gjk+w89lDlStdeEXx+dHNjQlKbau70M2nLv8Uy7uX896vvJdtI9uaHZIkSZKkBjFBn6Pzh/oB+N7IgcqVVlwIy86DB/6uQVGpna3oWcHNb7qZwc5Bfv5ffp6vPPGVZockSS3Oa9AlSQuDCfocXZAl6A/t3V+5UgRc8m54/A546rsNikztbM3AGj5/5ec5f8n5vP/r7+cTd36Cw+OHmx2WJLU8Z7hLktqZCfocLent4LyhPu5+4rnqFV/1bujsh6/8dmMCU9tb0bOCz135OX76op/mlodv4R3/+A6+tftbzQ5LkiRJ0jwxQa+DDWuXsfXxZ5motlBc33L4kV+DR/4Z7rq5ccGprXXmO/m1Db/GH7/pj5lIE1z35ev4wNc/wJ4De5odmiS1Dm+zJklaIEzQ62DDucsYPTLOd/eMVq/4ul+CF18FX/pV+OrHYbzK7dmkEpcOX8ptG2/j+ldezx277uDNt72Zj9/5cfYe3Nvs0CRJkiTViQl6HfzIhUPkAjZvP0WylMvDOz4Lr/gp+OaNcNNri6Pph59vSJxqb135Ln7hFb/AprduYuMFG/nCI1/gqr+7ihvuuIGte7eSHEGStIil7OrzmHafteQCcpKkNmKCXgfL+7u49LzlfHHbnurT3AEKXfC2T8G7vgA9S4uj6TeeD599C9zxe/DYN+DIKUbitagN9w/zm6/7Tf7p7f/ENRdewzd2foOf2fwzvPm2N/PJLZ9ky94tHD3u7AxJi4lJuCRpYSg0O4CF4toNZ/O+v7qH2+55kh9/9epTv2Dd5cXHk9+BBzfBI5tLFpALWLoGll9QfCw7HwbPgoFhGDgT+s8sJvpa1M7qP4tff+2v8/5L3s/mxzez+YnN3PLQLfz5d/+cQq7ARcsu4qUrXsr5S87nnCXnsHZwLSt7V5ILP5eTJEmSWtG8JugRcQXw+0Ae+JOU0u9MO94FfB54NfAM8M6U0uPzGdN8ecvLhvnMv36f39y0nXOH+rjk7DNO74WrLik+Lv8tOPQs7P5OMWkfeQie+R7sugWOlhlR7zkDepdD9xLoXlp87ll6YruzDzp6obO3+Dz16Ckp64F8F+Q7ireCU1vq7ejlbevextvWvY2Dxw5y1567uG/kPraNbOPvd/z9Sbdny0ee5d3LWdG7gqGeIVb0rGCwc5D+zn76O/oZ6BxgoHOA/o5+ejt66c5305nvpLvQTVe+i+58N4VcYcYUUklquuzfJf91kiS1s3lL0CMiD9wE/EdgF7AlIjallEpvBP5zwHMppQsi4lrgd4F3zldM8ymXC/7Puy7hJ/7oW7zj09/iqpcN88YLh1i7opeVA930dRXoKuTo7siTz1X486F3GVxwefExKSU4+DTs3w37n4IDe088H36ueP36kefh+SdObE+M1/AGOiDfWUzW851VtrPnXCF75CDyxevrc4Vse7KsUCyfOp4/jbq54gOy7cj24xT7s6kfp3G+kn0i+4sv+75NJael+2WOla03m2OUHDu9r90HXLbkxVy29CWw7p1MpMS+I8/wxIEnefzALvYeGuHpo88xcuQZ9u7fxfan72f/2AGOToxV/fEolYscXblOuvJddOW7KOQKdOQK5CNPIVegULLdEXnyuQKFXJ58FKaOF7Lj+ciTi9yJB0Eu8uQiiMiRjzwRQS5y5CNHkDu5fmT1CSJXrJ9j8rU5Yuq8xe9lsbWCyM4fWbtN/VeyPZv6nPRapupPfm8iOPGaXC6rxUlfb+pcpXWnzl+h7tTPyORTlBTNvB633PGTT1NyfCrZqfD6Mh/SnPL1tbzmVK8v8/5Odc5yr59NnKcsL1M0+bMuSZJUzXyOoG8AdqSUHgOIiFuAjUBpgr4R+K1s+1bgf0dEpDZd7eqspT380y+/gT/46qPcevcu/vG+3WXr5XPFP+lyMfnHeMkf8Nl+6bGTnZk9Xl4hikQPR6ce3dmjZ+p5jB6O0M0Y3Rylg3E6OUbHxDgdE+MUjo3TQbnHcTo5MLWf5zg5JigwQY4J8lPPxytsT5y0nfN6wYbIAS/KHq+tUm8M2J/LcSB7jOaCw7kcRyOmHkciGMuej+ZOlI8TjAeMRzBO8fl49nws4PDkcaJ4LNs+FnCcIAVMkD0imKB4NelxgonIth2xV5v76JrreMdlv9zsMBakD/7pldwZ/06sGaYvfYzfuKfAb2878WHIRJpoYnSSJM3OfCboq4CdJfu7mJkjTNVJKY1HxAvAcuDp0koRcR1wHcDZZ589X/HWxZLeDv7bWy7mI1ddxI6RAzz5/GFGRo9yaGyco+MTHDk2wdjx46RUTDwmso0EpJSmylPKjs2TceDAvJ391CJNEEyQS8eJVEzcc+k4wQSkRJA9sj+sgolsP1Fcq3eCSMVysvLIyk+cv1iW0kTV48WvN1H8oCRl55vx9Se/F+nk/VTpWLY99S0sc2zqUGLGeSfbKYuvfAzl4kknzjvjnKlCPEy955JDU23QBXSRGJwR80zT4z+h5NwlVSqn3TPPk7K2mCBl/1GyXb4cEhMxuc3U/0tbsvRYlJzj5PqpSn0qnn/6a8ptlb7m5LKZXxsgRSq7Yn8xnumtd/KZyr9m+tekTMmJvah4rvLnLxdJ+Zhre331qMucJ1KZdipfd+bB8m1yOq9fO/QD1c6sOTj/jJcz9sxRDucHeab/Al62egmd+ZNnK9zx5B0Mdg5yePwwawfX8tZ1b21StJIkVTefCXq5v72n/+1yOnVIKd0M3Aywfv36thh6zeWCC88c4MIzB5odiiRJC9Z73/67zQ5BkqS6mc8L4nYBa0r2VwPT53xP1YmIArAEeHYeY5IkSZIkqSXNZ4K+BVgXEedGRCdwLbBpWp1NwHuy7WuAr7br9eeSJEmSJM3FvE1xz64pvx7YTPE2a59JKW2PiI8BW1NKm4A/Bf48InZQHDm/dr7ikSRJkiSplc3rfdBTSrcDt08r+2jJ9hHgHfMZgyRJkiRJ7cCbskqSJEmS1AJM0CVJkiRJagEm6JIkSZIktQATdEmSJEmSWoAJuiRJkiRJLcAEXZIkVRQRV0TEwxGxIyJuKHP8FyPi/oi4NyL+NSIubkackiQtBCbokiSprIjIAzcBVwIXAz9ZJgH/y5TSy1JKrwQ+Cfxeg8OUJGnBMEGXJEmVbAB2pJQeSymNAbcAG0srpJRGS3b7gNTA+CRJWlAKzQ5gtu6+++6nI+KJOp92BfB0nc+5GNmO9WE71oftWB+2Y33Uux3PqeO5qlkF7CzZ3wW8dnqliPgl4ANAJ3BZuRNFxHXAddnugYh4uI5x+nNaH7ZjfdiO9WE71oftWB/z0Y5l+/K2S9BTSkP1PmdEbE0pra/3eRcb27E+bMf6sB3rw3asjzZuxyhTNmOEPKV0E3BTRPwU8BvAe8rUuRm4ue4R0tbt21Jsx/qwHevDdqwP27E+GtmOTnGXJEmV7ALWlOyvBnZXqX8L8NZ5jUiSpAXMBF2SJFWyBVgXEedGRCdwLbCptEJErCvZfTPwaAPjkyRpQWm7Ke7zZF6m3C1CtmN92I71YTvWh+1YH23Zjiml8Yi4HtgM5IHPpJS2R8THgK0ppU3A9RFxOXAMeI4y09sboC3btwXZjvVhO9aH7VgftmN9NKwdIyUXW5UkSZIkqdmc4i5JkiRJUgswQZckSZIkqQUs6gQ9Iq6IiIcjYkdE3NDseFpNRHwmIvZFxAMlZcsi4ssR8Wj2fEZWHhHxB1lbbouIS0pe856s/qMR0YxrE5sqItZExNci4sGI2B4R/yUrty1nISK6I+LbEXFf1o6/nZWfGxF3ZW3y19lCVkREV7a/Izu+tuRcH87KH46IH2vOO2quiMhHxD0R8cVs33acpYh4PCLuj4h7I2JrVubvdYPZl1dnXz539uP1YT9eX/bjc9ey/XhKaVE+KC528z3gPKATuA+4uNlxtdID+GHgEuCBkrJPAjdk2zcAv5ttXwV8ieI9cy8F7srKlwGPZc9nZNtnNPu9Nbgdh4FLsu0B4BHgYtty1u0YQH+23QHclbXP3wDXZuWfBt6bbf9n4NPZ9rXAX2fbF2e/713Audm/A/lmv78mtOcHgL8Evpjt246zb8PHgRXTyvy9buz3wL781G1kXz73NrQfr0872o/Xtz3tx+fehi3Zjy/mEfQNwI6U0mMppTGK927d2OSYWkpK6ZvAs9OKNwKfy7Y/x4n73W4EPp+K7gSWRsQw8GPAl1NKz6aUngO+DFwx/9G3jpTSnpTSd7Lt/cCDwCpsy1nJ2uNAttuRPRJwGXBrVj69HSfb91bgP0REZOW3pJSOppS+D+yg+O/BohERqyneDutPsv3AdqwXf68by778FOzL585+vD7sx+vHfnxeNf33ejEn6KuAnSX7u7IyVXdmSmkPFDssYGVWXqk9becS2bSiV1H81Ni2nKVsOte9wD6K/wB+D3g+pTSeVSltk6n2yo6/ACzHdgT4X8CHgIlsfzm2Yy0S8C8RcXdEXJeV+XvdWLZfbfw5rZH9+NzYj9eN/Xh9tGQ/vpjvgx5lyrznXO0qtaftnImIfuALwPtTSqPFDy/LVy1TZlsCKaXjwCsjYilwG3BRuWrZs+1YRkS8BdiXUro7It44WVymqu14aq9PKe2OiJXAlyPioSp1bcf5YfvVlz+nVdiPz539+NzZj9dVS/bji3kEfRewpmR/NbC7SbG0k6ey6Rxkz/uy8krtaTsDEdFBsVP/i5TS32XFtmWNUkrPA1+neA3Q0oiY/LCxtE2m2is7voTiNM/F3o6vB66OiMcpTge+jOIn8bbjLKWUdmfP+yj+obkBf68bzfarjT+ns2Q/Xl/243NiP14nrdqPL+YEfQuwLlvxsJPiogmbmhxTO9gETK5O+B7gH0rK352tcHgp8EI2LWQz8KaIOCNbBfFNWdmikV3n86fAgyml3ys5ZFvOQkQMZZ+4ExE9wOUUrwP8GnBNVm16O0627zXAV1NxNY9NwLXZqqbnAuuAbzfmXTRfSunDKaXVKaW1FP/d+2pK6V3YjrMSEX0RMTC5TfH38QH8vW40+/La+HM6C/bj9WE/Xh/24/XR0v14aoEV9Jr1oLga3yMUr3/5SLPjabUH8FfAHuAYxU+Hfo7iNStfAR7NnpdldQO4KWvL+4H1Jef5WYoLT+wAfqbZ76sJ7fhDFKe6bAPuzR5X2ZazbseXA/dk7fgA8NGs/DyKHcoO4G+Brqy8O9vfkR0/r+RcH8na92Hgyma/tya26RsVyfDNAAADFUlEQVQ5sfqr7Ti7tjuP4uq39wHbJ/sQf6+b8r2wL6/ePvblc29D+/H6tKP9eP3b1H689rZr2X48spNKkiRJkqQmWsxT3CVJkiRJahkm6JIkSZIktQATdEmSJEmSWoAJuiRJkiRJLcAEXZIkSZKkFmCCLi1QEXEge14bET9V53P/+rT9/1fP80uStNjZj0uLkwm6tPCtBWbVsUdE/hRVTurYU0o/OMuYJEnS6VmL/bi0aJigSwvf7wBviIh7I+JXIiIfETdGxJaI2BYRvwAQEW+MiK9FxF8C92dlfx8Rd0fE9oi4Liv7HaAnO99fZGWTn/JHdu4HIuL+iHhnybm/HhG3RsRDEfEXERGT54uI72ax/I+Gt44kSa3NflxaRArNDkDSvLsB+GBK6S0AWQf9QkrpNRHRBfxbRPxLVncD8NKU0vez/Z9NKT0bET3Aloj4Qkrphoi4PqX0yjJf6+3AK4FXACuy13wzO/Yq4AeA3cC/Aa+PiO8CbwNeklJKEbG07u9ekqT2Zj8uLSKOoEuLz5uAd0fEvcBdwHJgXXbs2yWdOsD7IuI+4E5gTUm9Sn4I+KuU0vGU0lPAN4DXlJx7V0ppAriX4pS9UeAI8CcR8Xbg0JzfnSRJC5v9uLSAmaBLi08Av5xSemX2ODelNPnJ+8GpShFvBC4HXpdSegVwD9B9Gueu5GjJ9nGgkFIap/hp/xeAtwL/PKt3IknS4mM/Li1gJujSwrcfGCjZ3wy8NyI6ACLiwojoK/O6JcBzKaVDEfES4NKSY8cmXz/NN4F3ZtfHDQE/DHy7UmAR0Q8sSSndDryf4rQ6SZJ0gv24tIh4Dbq08G0DxrMpbp8Ffp/itLTvZAu8jFD81Hu6fwZ+MSK2AQ9TnB436WZgW0R8J6X0rpLy24DXAfcBCfhQSmlv9odBOQPAP0REN8VP7X+ltrcoSdKCZT8uLSKRUmp2DJIkSZIkLXpOcZckSZIkqQWYoEuSJEmS1AJM0CVJkiRJagEm6JIkSZIktQATdEmSJEmSWoAJuiRJkiRJLcAEXZIkSZKkFvD/Ad5PnsCEQ7AAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_records, acc_records = exp_activation(4, 3, X_train, y_train, X_test, y_test)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot(loss_records[0])\n",
    "ax[0].plot(loss_records[1])\n",
    "ax[0].plot(loss_records[2])\n",
    "ax[0].legend(('ReLU', 'Tanh', 'Sigmoid'), loc='upper right')\n",
    "ax[0].set_xlabel('Iterations')\n",
    "ax[0].set_ylabel('Loss')\n",
    "\n",
    "ax[1].plot(acc_records[0])\n",
    "ax[1].plot(acc_records[1])\n",
    "ax[1].plot(acc_records[2])\n",
    "ax[1].legend(('ReLU', 'Tanh', 'Sigmoid'), loc='upper right')\n",
    "ax[1].set_xlabel('Iterations')\n",
    "ax[1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.tight_layout()"
   ]
  }
 ],
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